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Assuring Large Language Models 

LLMs you Can Trust to Speak on your Behalf.

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If thought corrupts language, language can also corrupt thought.

- George Orwell

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Large Language Models speak on behalf of your business. The words they choose have consequences.

Advai brings unique expertise with adversarial methods to tackle the complex world of LLMs. 

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Large Language Model guardrails keep LLMs within strict parameters the business can control and monitor.

  1. Ethical alignment

    Integrate your business ethics and objectives directly into the LLM.

  2. Organisational lingua franca

    Terminology, names, positions, product and process information can be encoded into model via finetuning. 

  3. Adversarial resistence

    Generally, the way to withstand adversarial attacks is by first attacking it, then building guardrails to target these weaknesses. 

  4. Compliance requirements

    High-impact applications involving language models come with strict compliance requirements. Guardrails should therefore be customised to meet these requirements. 

What's involved?

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  1. We jailbreak language models

    Using advanced adversarial techniques, we algorithmically explore for vulnerabilities, revealing how your language model will fail. 

  2. We create operational boundaries

    Once you know where your system will fail, you can create guidelines for the system's use. 

  3. We fine tune the reward model

    We assess your reward models, to ensure the goals and preferences are reflected in model behaviour.

Our LLM Alignment Framework

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Leverage the Alignment framework to de risk and secure the outputs of generative AI.

  1. Retrieval-Augmented Generation

    Using RAG Helps improve the accuracy of the content returned and reduces the chance of hallucination. Enables the incorporation of company, technical or industry specific lingo.

  2. Guardrails

    Guardrails are primarily there to protect sensitive data; however, their role also includes fact checking, response moderation and sourcing - citing evidence for responses. 

  3. Prompt Engineering

    Prompt engineering is a well established technique for improving the quality of generative AI responses. Our framework provides easy access to a Prompt Library, Structural guidance, Company Profiles, and formats for critiquing responses.

  4. User Interface

    The interface makes it easy to select the relevant data the response should resource, facilitates input drafting and review, and can include templates for sign-off. 

What's a reward model?

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  1. Foundation model

    A base model, usually trained in an unsupervised manner.

  2. Reward model

    A model that can score foundation model outputs on how well they align with human preferences. Rewards models can vary in model type but often they are traditional machine learning models such as classifiers.

  3. Alignment prompts dataset

    A dataset of prompts, that reflect inputs where the model should be aligned. This will benefit from earlier adversarial attack optimisations.

Benefits

Risk appropriate control over your LLM.

Adjust controls depending on the use-case and context of your model deployment. Risk appetites will differ between customer facing tools and the internal tools. 

Enable key internal stakeholders to grasp how AI language models interpret knowledge and form responses.

Well managed risk will promote trust, stakeholder confidence and user adoption.

Meet stringent compliance requirements.

Advai Guardrails come complete with end-to-end documentation that highlights the rigorous robustness assurance methods employed. 

This acts as a safety net against regulatory challenges and assures that your organisation's AI operations are both safe and compliant.

With Advai's robust alignment and testing, enjoy peace of mind knowing that your LLM will function as intended in various scenarios.

Keep your LLM guardrails up-to-date.

As a cutting edge field, novel methods to control LLMs are discovered every week.

Our team of researchers and ML engineers keep your LLM guardrails updated.

Adversarial attack methods are released almost weekly. Keeping on top of novel attack vectors will reduce the chance of your business saying something it will regret. 

Deploy faster with confidence

Meet the competitive pressure to deploy without undue risk.

Assurance needs to come first, not last. The faster you can assure your system, the faster you can deploy. 

Ensure the reliability of your Large Language Models (LLMs) with our comprehensive robustness assessments.

Win the confidence of key stakeholders using empirical methods to demonstrate that your model is fit for purpose.

 

  • Our adversarial attacks have been optimised across multiple models with different LLM architectures, therefore having relevance to a broader landscape of verification methods. We have demonstrated that this enables us to successfully conduct “one shot” attacks on multiple unrelated systems.

  • We enable businesses to fine-tune and control Large Language Models (LLMs) to align with organisational risk appetites and operational requirements.

  • Our approach places a heavy emphasis on ensuring the quality of reward models used in LLM fine-tuning. We counter this by using algorithmically optimised suffix attacks (see more below).

  • We carry out advanced self-optimising suffix attacks to discover out-of-sample attack vectors (unfamiliar strings of text input) that reveal novel methods of bypassing guardrails and manipulating LLMs to perform undesirably. This reveals vulnerabilities to address. 

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‘Low-hanging fruit’ integration: Address high impact & ‘best-for-LLM’ tasks, then expand and advance in complexity.

Recognise weaknesses of AI: targeted human in-the-loop.

  • Team: PM, CTO, ML Engineer, 2 x Data Scientists
  • Objective: Target high-impact implementations
  • Deliverable: LLM-enabled service with a simple UI
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Industry state-of-the-art approach: Plan and integrate on modular basis, expecting the tech to rapidly evolve.

Collaboration: ensure your team understands what is going on, and that analysts understand responsibilities and opportunities.

  • Cost: Tailored to client needs and Phase 1 findings
  • Objective: Modular and prioritised implementations
  • Deliverables: Proof-of-Concept, further discovery, and continuous updates

Roadmap.

What to expect from our LLM Assurance process. 

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